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Abstract-This paper presents a novel way for detecting sign board and text recognition to aid navigation in indoor environment . Using text as a landmark for vision based navigation is still an active research and till date all algorithms developed for detection and recognition of texts for an indoor navigation have a lot of room to make it applicable on real time. Our proposed method is an extension...
Relevance feedback has been employed in Content Based Image Retrieval systems to bridge the semantic gap between the low level features and high level semantics of the image. This paper proposes a short term learning relevance feedback algorithm that utilizes the statistical features of the feedback images for determining the relevance of the candidate image in the next iteration and for achieving...
In this paper, we propose a component-based object detection method extended with the fuzzy inference technique. The proposed method detects constituent components of a complex object instead of a whole object in images. For component detection, multiple multi-class support vector machines (SVM) are used in parallel. Each SVM classifies the candidate component using a different low-level image feature...
Image classification is currently a vital and challenging topic in computer vision. Although it has been achieved many classification algorithms so far, the classification of natural images still remains great difficulties in image processing. In this paper, we propose a semantic linear-time graph kernel for image classification. Each image is represented by a graph and the vertex of each graph corresponds...
This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent...
Inspired by the effectiveness of global priors for 2D saliency analysis, this paper aims to explore those particular to RGB-D data. To this end, we propose two priors, which are the normalized depth prior and the global-context surface orientation prior, and formulate them in the forms simple for computation. A two-stage RGB-D salient object detection framework is presented. It first integrates the...
Content based image retrieval techniques have been studied extensively in the past years due to the exponential growth of digital image information available in recent years with the widespread use of internet and declining cost of storage devices. Many techniques such as relevance feedback, multi query systems, etc. have been employed in CBIR systems to bridge the semantic gap between the low level...
This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation...
Existing scientific workflow tools, created by computer scientists, require that domain scientists meticulously design their multi-step experiments before analyzing data. However, this is oftentimes contradictory to a domain scientist's routine of conducting research and exploration. This paper presents a novel way to resolve this dispute, in the context of service-oriented science. After scrutinizing...
We propose a method to extract user attributes from the pictures posted in social media feeds, specifically gender information. While traditional approaches rely on text analysis or exploit visual information only from the user profile picture or colors, we propose to look at the distribution of semantics in the pictures coming from the whole feed of a person to estimate gender. In order to compute...
In this paper we tackle the challenges of visual tracking for personal robots. We have proposed a novel track-by-detection method that combines a semantic object model with depth properties to obtain target contours. The tracking can be initialized by either 2D or 3D inputs, which are further refined using clustering based background removal to obtain an initial object model. During tracking, we propose...
The problem of content based image retrieval is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in the relevance feedback scheme. We try to maximize the margin between relevant and irrelevant samples at local neighborhoods. In the reduced...
Clothing attributes, of which color plays an important role, are receiving more and more interests in machine vision researches and applications because of their uses and effectiveness in tasks like pedestrian analysis. However, color description is a challenging problem due to complex environments such as illumination variations. Most prior works describe color attributes using only low-level features...
The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, however many of this systems are likely to fail due to use global features which cannot sufficiently capture the important properties of individual objects [1] because generally a typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant...
In this paper, a scheme for interactive video segmentation is presented. A key feature of the system is the distinction between two levels of segmentation, namely Region and Object Segmentation. Regions are homogeneous areas of the images, which are extracted automatically by the computer. Semantically meaningful objects are obtained through user interaction by grouping of regions, according to application...
In this paper a novel approach is proposed for semantic video object segmentation. In particular, it is assumed that several neural network structures have been stored in a system database or memory. Each network have been learned to be appropriate to a specific application. Then, a retrieval mechanism is introduced which selects that network from the memory which better approximates the current environment...
It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to relate and easy to understand. However such queries are not easily utilised within intelligent surveillance systems as they are difficult to transform into a representation that can be...
A novel framework for pruning a category of images is proposed in this paper. We assume no prior information about the contents or semantics of the images. Our framework discovers consistencies and knowledge about the spatial relations of the categories unsupervised using iterative image segmentation and spatial grouping. A measure for deciding how well an image fits to a category is proposed and...
Despite the tremendous importance and availability of large video collections, support for video retrieval is still rather limited and is mostly tailored to very concrete use cases and collections. In image retrieval, for instance, standard keyword search on the basis of manual annotations and content-based image retrieval, based on the similarity to query image (s), are well established search paradigms,...
Nowadays, Content Based Image retrieval (CBIR) has been one of the most interesting research areas among researchers of technology-giants and academic institutions. Various CBIR techniques in literature show that there are three key properties in image retrieval viz. color, texture and shape. In this paper a detailed classification along with the challenges and issues of CBIR techniques evolved during...
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